6 commits

Author	SHA1	Message	Date
Sébastien Han	d82a2cd6f8	fix: httpcore deadlock in CI by properly closing streaming responses (#4335) ... # What does this PR do? The test_conversation_error_handling test was timing out in CI with a deadlock in httpcore's connection pool. The root cause was the preceding test_conversation_multi_turn_and_streaming test, which broke out of the streaming response iterator early without properly closing the underlying HTTP connection. When a streaming response iterator is abandoned mid-stream, the HTTP connection remains in an incomplete state. Since the openai_client fixture is session-scoped, subsequent tests reuse the same httpcore connection pool. The dangling connection causes the pool's internal lock to deadlock when the next test attempts to acquire a new connection. The fix wraps the streaming response in a context manager, which ensures the connection is properly closed when exiting the with block, even when breaking out of the loop early. This is a best practice when working with streaming HTTP responses that may not be fully consumed. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-12-08 16:38:46 +01:00
Omar Abdelwahab	eb545034ab	fix: MCP authorization parameter implementation (#4052) ... # What does this PR do? Adding a user-facing `authorization ` parameter to MCP tool definitions that allows users to explicitly configure credentials per MCP server, addressing GitHub Issue #4034 in a secure manner. ## Test Plan tests/integration/responses/test_mcp_authentication.py --------- Co-authored-by: Omar Abdelwahab <omara@fb.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-14 08:54:42 -08:00
Ashwin Bharambe	1e81056a22	feat(tests): enable MCP tests in server mode (#4146) ... We would like to run all OpenAI compatibility tests using only the openai-client library. This is most friendly for contributors since they can run tests without needing to update the client-sdks (which is getting easier but still a long pole.) This is the first step in enabling that -- no using "library client" for any of the Responses tests. This seems like a reasonable trade-off since the usage of an embeddeble library client for Responses (or any OpenAI-compatible) behavior seems to be not very common. To do this, we needed to enable MCP tests (which only worked in library client mode) for server mode.	2025-11-13 07:23:23 -08:00
Charlie Doern	37853ca558	fix(tests): add OpenAI client connection cleanup to prevent CI hangs (#4119) ... # What does this PR do? Add explicit connection cleanup and shorter timeouts to OpenAI client fixtures. Fixes CI deadlock after 25+ tests due to connection pool exhaustion. Also adds 60s timeout to test_conversation_context_loading as safety net. ## Test Plan tests pass Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-12 12:17:13 -05:00
Ashwin Bharambe	8e7e0ddfec	fix(responses): use conversation items when no stored messages exist (#3819) ... Handle a base case when no stored messages exist because no Response call has been made. ## Test Plan ``` ./scripts/integration-tests.sh --stack-config server:ci-tests \ --suite responses --inference-mode record-if-missing --pattern test_conversation_responses ```	2025-10-15 14:43:44 -07:00
Francisco Arceo	e7d21e1ee3	feat: Add support for Conversations in Responses API (#3743) ... # What does this PR do? This PR adds support for Conversations in Responses. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan Unit tests Integration tests <Details> <Summary>Manual testing with this script: (click to expand)</Summary> ```python from openai import OpenAI client = OpenAI() client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none") def test_conversation_create(): print("Testing conversation create...") conversation = client.conversations.create( metadata={"topic": "demo"}, items=[ {"type": "message", "role": "user", "content": "Hello!"} ] ) print(f"Created: {conversation}") return conversation def test_conversation_retrieve(conv_id): print(f"Testing conversation retrieve for {conv_id}...") retrieved = client.conversations.retrieve(conv_id) print(f"Retrieved: {retrieved}") return retrieved def test_conversation_update(conv_id): print(f"Testing conversation update for {conv_id}...") updated = client.conversations.update( conv_id, metadata={"topic": "project-x"} ) print(f"Updated: {updated}") return updated def test_conversation_delete(conv_id): print(f"Testing conversation delete for {conv_id}...") deleted = client.conversations.delete(conv_id) print(f"Deleted: {deleted}") return deleted def test_conversation_items_create(conv_id): print(f"Testing conversation items create for {conv_id}...") items = client.conversations.items.create( conv_id, items=[ { "type": "message", "role": "user", "content": [{"type": "input_text", "text": "Hello!"}] }, { "type": "message", "role": "user", "content": [{"type": "input_text", "text": "How are you?"}] } ] ) print(f"Items created: {items}") return items def test_conversation_items_list(conv_id): print(f"Testing conversation items list for {conv_id}...") items = client.conversations.items.list(conv_id, limit=10) print(f"Items list: {items}") return items def test_conversation_item_retrieve(conv_id, item_id): print(f"Testing conversation item retrieve for {conv_id}/{item_id}...") item = client.conversations.items.retrieve(conversation_id=conv_id, item_id=item_id) print(f"Item retrieved: {item}") return item def test_conversation_item_delete(conv_id, item_id): print(f"Testing conversation item delete for {conv_id}/{item_id}...") deleted = client.conversations.items.delete(conversation_id=conv_id, item_id=item_id) print(f"Item deleted: {deleted}") return deleted def test_conversation_responses_create(): print("\nTesting conversation create for a responses example...") conversation = client.conversations.create() print(f"Created: {conversation}") response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": "What are the 5 Ds of dodgeball?"}], conversation=conversation.id, ) print(f"Created response: {response} for conversation {conversation.id}") return response, conversation def test_conversations_responses_create_followup( conversation, content="Repeat what you just said but add 'this is my second time saying this'", ): print(f"Using: {conversation.id}") response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": content}], conversation=conversation.id, ) print(f"Created response: {response} for conversation {conversation.id}") conv_items = client.conversations.items.list(conversation.id) print(f"\nRetrieving list of items for conversation {conversation.id}:") print(conv_items.model_dump_json(indent=2)) def test_response_with_fake_conv_id(): fake_conv_id = "conv_zzzzzzzzz5dc81908289d62779d2ac510a2b0b602ef00a44" print(f"Using {fake_conv_id}") try: response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": "say hello"}], conversation=fake_conv_id, ) print(f"Created response: {response} for conversation {fake_conv_id}") except Exception as e: print(f"failed to create response for conversation {fake_conv_id} with error {e}") def main(): print("Testing OpenAI Conversations API...") # Create conversation conversation = test_conversation_create() conv_id = conversation.id # Retrieve conversation test_conversation_retrieve(conv_id) # Update conversation test_conversation_update(conv_id) # Create items items = test_conversation_items_create(conv_id) # List items items_list = test_conversation_items_list(conv_id) # Retrieve specific item if items_list.data: item_id = items_list.data[0].id test_conversation_item_retrieve(conv_id, item_id) # Delete item test_conversation_item_delete(conv_id, item_id) # Delete conversation test_conversation_delete(conv_id) response, conversation2 = test_conversation_responses_create() print('\ntesting reseponse retrieval') test_conversation_retrieve(conversation2.id) print('\ntesting responses follow up') test_conversations_responses_create_followup(conversation2) print('\ntesting responses follow up x2!') test_conversations_responses_create_followup( conversation2, content="Repeat what you just said but add 'this is my third time saying this'", ) test_response_with_fake_conv_id() print("All tests completed!") if __name__ == "__main__": main() ``` </Details> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-10 11:57:40 -07:00